Impact of the drift length on the performance of MR-ToF devices
Bibliographic record
Abstract
Multi-Reflection Time-of-Flight (MR-ToF) devices are powerful tools for high-precision mass spectrometry, highly selective and high-flux mass separation as well as, most recently, for highly sensitive laser spectroscopic applications. In this work, various ion-optical simulations are conducted to provide insights into optimizing the design of the MR-ToF apparatus to enhance performance across its diverse application fields. In particular, we investigate the effect of the drift length, i.e. the distance between the two electrostatic mirrors of an MR-ToF device, which reveals that an optimal mass resolving power can only be achieved through matching of the drift-tube length, ion beam energy, and the design of the electrostatic mirrors. This finding can be well understood by analytical one-dimensional models which fit remarkably well to existing MR-ToF devices. For some of the applications enabled by MR-ToF devices, precise knowledge about the ion-beam energy is of importance. We find that the revolution period as a function of the kinetic energy of the ions not only serves as an experimental benchmark for our simulation studies but also allows to determine the beam energy of the stored ions. This is experimentally demonstrated to an accuracy of 5 eV for an MR-ToF device operated at 1.5 keV beam energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".